


import itertools
import random
import unittest

import numpy as np

from transformers import ASTFeatureExtractor
from transformers.testing_utils import require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


__UpperCamelCase     		=							random.Random()

if is_torch_available():
						import torch
def 							UpperCamelCase_(					_A :int					,  _A :Any=1.0					,  _A :Any=None					,  _A :List[str]=None	)->		Union[str, Any]:
			if rng is None:
						UpperCamelCase__       =							global_rng

			UpperCamelCase__       =							[]
			for batch_idx in range(shape[0]	):
						values.append([]	)
						for _ in range(shape[1]	):
									values[-1].append(rng.random() * scale	)

			return values







class 							lowerCamelCase__						(				unittest.TestCase   ):






			"""simple docstring"""






			def __init__(				self   ,    snake_case   ,    snake_case=7   ,    snake_case=400   ,    snake_case=2000   ,    snake_case=1   ,    snake_case=0.0   ,    snake_case=16000   ,    snake_case=True   ,    snake_case=True   ,    ):

						'''simple docstring'''


						UpperCamelCase__       =							parent
						UpperCamelCase__       =							batch_size
						UpperCamelCase__       =							min_seq_length
						UpperCamelCase__       =							max_seq_length
						UpperCamelCase__       =							(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
						UpperCamelCase__       =							feature_size
						UpperCamelCase__       =							padding_value
						UpperCamelCase__       =							sampling_rate
						UpperCamelCase__       =							return_attention_mask
						UpperCamelCase__       =							do_normalize




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						return {
						    "feature_size": self.feature_size,
						    "padding_value": self.padding_value,
						    "sampling_rate": self.sampling_rate,
						    "return_attention_mask": self.return_attention_mask,
						    "do_normalize": self.do_normalize,
						}







			def 					snake_case__					(				self   ,    snake_case=False   ,    snake_case=False  ):

						'''simple docstring'''


						def _flatten(snake_case  ):
									return list(itertools.chain(*snake_case  )  )

						if equal_length:
									UpperCamelCase__       =							floats_list((self.batch_size, self.max_seq_length)  )
						else:
									# make sure that inputs increase in size
									UpperCamelCase__       =							[
									    _flatten(floats_list((x, self.feature_size)  )  )
									    for x in range(self.min_seq_length   ,    self.max_seq_length   ,    self.seq_length_diff  )
									]

						if numpify:
									UpperCamelCase__       =							[np.asarray(snake_case  ) for x in speech_inputs]

						return speech_inputs







@require_torch
@require_torchaudio
class 							lowerCamelCase__						(				UpperCAmelCase       ,						unittest.TestCase   ):






			"""simple docstring"""






			_UpperCamelCase		:       List[str] 			=		ASTFeatureExtractor




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__       =							ASTFeatureExtractionTester(self  )




			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__       =							self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()  )
						# create three inputs of length 800, 1000, and 1200
						UpperCamelCase__       =							[floats_list((1, x)  )[0] for x in range(800   ,    1400   ,    200  )]
						UpperCamelCase__       =							[np.asarray(snake_case  ) for speech_input in speech_inputs]

						# Test not batched input
						UpperCamelCase__       =							feat_extract(speech_inputs[0]   ,    return_tensors="np"  ).input_values
						UpperCamelCase__       =							feat_extract(np_speech_inputs[0]   ,    return_tensors="np"  ).input_values
						self.assertTrue(np.allclose(snake_case   ,    snake_case   ,    atol=1E-3  )  )

						# Test batched
						UpperCamelCase__       =							feat_extract(snake_case   ,    padding=snake_case   ,    return_tensors="np"  ).input_values
						UpperCamelCase__       =							feat_extract(snake_case   ,    padding=snake_case   ,    return_tensors="np"  ).input_values
						for enc_seq_a, enc_seq_a in zip(snake_case   ,    snake_case  ):
									self.assertTrue(np.allclose(snake_case   ,    snake_case   ,    atol=1E-3  )  )

						# Test 2-D numpy arrays are batched.
						UpperCamelCase__       =							[floats_list((1, x)  )[0] for x in (800, 800, 800)]
						UpperCamelCase__       =							np.asarray(snake_case  )
						UpperCamelCase__       =							feat_extract(snake_case   ,    return_tensors="np"  ).input_values
						UpperCamelCase__       =							feat_extract(snake_case   ,    return_tensors="np"  ).input_values
						for enc_seq_a, enc_seq_a in zip(snake_case   ,    snake_case  ):
									self.assertTrue(np.allclose(snake_case   ,    snake_case   ,    atol=1E-3  )  )




			@require_torch
			def 					snake_case__					(				self  ):

						'''simple docstring'''


						import torch

						UpperCamelCase__       =							self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()  )
						UpperCamelCase__       =							np.random.rand(100  ).astype(np.floataa  )
						UpperCamelCase__       =							np_speech_inputs.tolist()

						for inputs in [py_speech_inputs, np_speech_inputs]:
									UpperCamelCase__       =							feature_extractor.pad([{"input_values": inputs}]   ,    return_tensors="np"  )
									self.assertTrue(np_processed.input_values.dtype == np.floataa  )
									UpperCamelCase__       =							feature_extractor.pad([{"input_values": inputs}]   ,    return_tensors="pt"  )
									self.assertTrue(pt_processed.input_values.dtype == torch.floataa  )




			def 					snake_case__					(				self   ,    snake_case  ):

						'''simple docstring'''


						from datasets import load_dataset

						UpperCamelCase__       =							load_dataset("hf-internal-testing/librispeech_asr_dummy"   ,    "clean"   ,    split="validation"  )
						# automatic decoding with librispeech
						UpperCamelCase__       =							ds.sort("id"  ).select(range(snake_case  )  )[:num_samples]["audio"]

						return [x["array"] for x in speech_samples]







			@require_torch
			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__       =							torch.tensor(
						    [-0.9894, -1.2776, -0.9066, -1.2776, -0.9349, -1.2609, -1.0386, -1.2776,
						     -1.1561, -1.2776, -1.2052, -1.2723, -1.2190, -1.2132, -1.2776, -1.1133,
						     -1.1953, -1.1343, -1.1584, -1.2203, -1.1770, -1.2474, -1.2381, -1.1936,
						     -0.9270, -0.8317, -0.8049, -0.7706, -0.7565, -0.7869]  )
						# fmt: on

						UpperCamelCase__       =							self._load_datasamples(1  )
						UpperCamelCase__       =							ASTFeatureExtractor()
						UpperCamelCase__       =							feature_extractor(snake_case   ,    return_tensors="pt"  ).input_values
						self.assertEquals(input_values.shape   ,    (1, 1024, 128)  )
						self.assertTrue(torch.allclose(input_values[0, 0, :30]   ,    snake_case   ,    atol=1E-4  )  )






def 							UpperCamelCase_(					_A :Union[str, Any]	)->		List[str]:
			UpperCamelCase__       =							[0] * len(_A	)
			UpperCamelCase__       =							[]
			UpperCamelCase__       =							[]
			UpperCamelCase__       =							0

			for values in graph.values():
						for i in values:
									indegree[i] += 1

			for i in range(len(_A	)	):
						if indegree[i] == 0:
									queue.append(_A	)

			while queue:
						UpperCamelCase__       =							queue.pop(0	)
						cnt += 1
						topo.append(_A	)
						for x in graph[vertex]:
									indegree[x] -= 1
									if indegree[x] == 0:
												queue.append(_A	)

			if cnt != len(_A	):
						print("Cycle exists"	)
			else:
						print(_A	)


# Adjacency List of Graph
__UpperCamelCase     		=							{0: [1, 2], 1: [3], 2: [3], 3: [4, 5], 4: [], 5: []}







topological_sort(graph)


